[robotics-worldwide] [meetings] Final CFP: IROS Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics 2016 (ML-HLCR 2016)

Tadahiro Taniguchi taniguchi at em.ci.ritsumei.ac.jp
Sun Aug 14 09:47:19 PDT 2016

(We apologize if you receive multiple copies of this message)

We kindly invite you to contribute to the IROS 2016 Workshop on
Machine Learning Methods for High-Level Cognitive Capabilities in

The deadline is extended to August 22, 2016.

Workshop on Machine Learning Methods for High-Level Cognitive
Capabilities in Robotics 2016
(Full-day workshop)
October 14th 2016
Daejeon, Korea.


*****DEADLINE IS EXTENDED TO August 22, 2016 *******

We kindly invite you to submit your contributions to this workshop.
For the detailed information, please visit our website.

*Abstract of the workshop
Integrating multi-level sensory-motor and cognitive capabilities is
essential for developing robotic systems that can adaptively act in
our daily environment in active collaboration with humans. In this
workshop, we aim to  share knowledge about the state-of-the-art
machine learning methods that contribute to modeling sensory-motor and
cognitive capabilities in robotics and to exchange views among
cutting-edge robotics researchers with a special emphasis on adaptive
high-level cognition.
Our daily environment is full of uncertainties with complex objects
and challenging tasks. A robot is not only required to deal with
things appropriately in a physical manner but also required to perform
logical and/or linguistic tasks in the real world. Conventionally,
symbol-based and/or rule-based approaches have been employed to model
high-level cognitive capabilities in robotics. However, it has been
pointed out that such conventional methods could not deal with the
uncertainty that is inevitably found in the physical environment and
natural human-robot communication.
Recent advances in machine learning techniques, including deep
learning and hierarchical Bayesian modeling, are providing us with new
possibilities to integrate high-level and low-level cognitive
capabilities in robotics. It became clear that such learning methods
are indispensable to create robots that can effectively deal with
uncertainty while acting smart in the real world.
In this workshop, we will investigate how to create synergies so that
advanced learning of sensorimotor and cognitive capabilities can
interact to create a bootstrapping effect in different levels of skill

*Topics of interest

Multimodal machine learning for robotics
Deep learning for robotics
Computational approaches to the study of development and learning
Bayesian modeling for high-level cognitive capabilities
Emergence of communication
Segmentation of time-series information
Probabilistic programming and reasoning
Language acquisition
Symbol grounding
Human-robot communication and collaboration based on machine learning
Human-assisted learning
Imitation learning and Skill acquisition
Cognitive and perceptual development
Exploration and learning in animals and robots
Social and emotional learning in humans and robots
Curiosity and intrinsic motivation
Affordance learning

The topics of the contributed papers are not limited to the topics shown above.

*Call for contributions
Participants are required to submit a contribution as:

- Extended abstract (maximum 2 pages in length)

All submissions will be reviewed on the basis of relevance, novelty,
originality, significance, soundness and clarity. At least two
referees will review each submission independently.
Accepted papers will be presented during the workshop in a poster session.
A small number of selected papers will be presented as oral
presentations or spotlight talks.

Submissions must be in PDF following the IEEE conference style in two-columns.
Send your PDF manuscript indicating [ML-HLCR 2016] in the subject to
the following emai:


*Important dates*
August 22, 2016 (EXTENDED)- Contributions submission deadline
August 31, 2016 - Notification of acceptance
October 14, 2016 - Workshop

*Invited speakers

Jun Tani, KAIST
Komei Sugiura, NICT
Xavier Hinaut, INRIA
Justus Piater, University of Innsbruck
Tadahiro Taniguchi, Ritsumeikan University
Kuniaki Noda, Nissan North America


Takayuki Nagai, The University of Electro-Communications
Tetsuya Ogata, Waseda University
Emre Ugur, Bogazici University
Yiannis Demiris, Imperial College London
Tadahiro Taniguchi, Ritsumeikan University, Japan,

See more details in:

Tadahiro Taniguchi
PhD. Eng. (Kyoto Univ.) / Registered Management Consultant
Associate Professor, Department of Human & Computer Intelligence
College of Information Science and Engineering, Ritsumeikan University
1-1-1 Noji Higashi, Kusatsu, Shiga 525-8577, Japan
Email: tadahiro at tanichu.com / taniguchi at ci.ritsumei.ac.jp
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Personal URL: https://urldefense.proofpoint.com/v2/url?u=http-3A__tanichu.com_&d=DQIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=1IxxOuWkRXwTTKIz6RWCuJFNn5gExEjWpMrVNzOoTKw&s=M6ptnCchb3TKWarmE-0kd8LzQbG9vJHM-aQfq2VxMOk&e= 
twitter id: @tanichu
Visiting Associate Professor
ISN Group, Electrical and Electronic Engineering
Imperial College London
Exhibition Road, London, SW7 2AZ
(Oct.2015 - Oct.2016)
TEL +44-7492-989851
Emergent Systems Laboratory
TEL: +81-77-561-5745 (A staff or a student)
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